We conducted our experiments on two egocentric video datasets with gaze tracking data to serve as ground truth – EGTEA Gaze+ [1]} and Ego4D [2]}. The EGTEA dataset is captured under the meal preparation setting, which involves a great deal of hand-object interactions. We used the first train/test split from EGTEA in our experiments (8299 clips for training and 2022 clips for testing). The Ego4D dataset includes 27 videos of 80 participants totaling 31 hours with gaze tracking data captured under the social setting. We split the long videos into 5-second video clips and pick clips containing gaze fixation. More details of the selection of clips with gaze fixations are discussed in supplementary. We used 20 videos (15310 clips) for training and the other 7 videos (5202 clips) for testing. This is the first work that uses the Ego4D dataset for egocentric gaze estimation, and we will make our split publicly available to drive future research on this topic. Following [1]}, [4]}, we adopt F1 score, recall, and precision as the evaluation metrics.
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